Abstract
Innovation adoption is a widely understudied area. However, there has been a substantial amount of arguments concerned with the extent to which personality traits affect innovative behavior. Thus, the purpose of this study is to examine the extent to which personality traits, namely time orientation and innate innovativeness, influence intentioned adoption of the specific innovation – the virtual nurse assistant. The organisation of the chapter is as follows: The first part of the chapter introduces the study to the reader and starts with the research background, followed by the research problem, rationale of the study, and research objectives. The second part consists of literature review, critically evaluating the relevant secondary sources to derive the research hypothesis. The third part represents the results and findings of the study, followed by the conclusion and recommendations as the closing part of this chapter.
TopIntroduction
Today it is impossible to imagine humanity evolving without artificial intelligence (AI) (Lowe et al., 2019; Miller, 2019). Smart cities, self-driving cars and humanoid robots are the evidence of the ability of AI to further revolutionise industries and alter the consumption patterns (Bundy, 2017). Healthcare is one of the sectors where AI has already brought huge opportunities (Jiang, F et al., 2017). A report by Accenture (Fu et al., 2017) defines AI in healthcare as “a collection of multiple technologies, which enable machines to sense, comprehend, act and learn, so they perform clinical healthcare functions.”
Virtual nurse assistant (VNA) is one of the innovations in the healthcare market inspired by AI. This is an advanced technology in mobile health (m-Health) services, namely a concept of healthcare practices provided through tablets and cell phones (Zhao et al., 2018). AI-powered virtual nurse avatar provides medical services through the mobile application aiming to reduce unnecessary visits to the practitioner. At present, the VNA can assess symptoms and give limited medical advice during a verbal or text message communication with the consumer (Figure 1). Additionally, this innovation can continuously monitor the consumer’s health through wearable technology and update personal medical data as well as alert medical professionals if necessary (Ram, 2018). However, the biggest advantage and the future potential of this innovation is in self-learning and the prediction of a patient’s health condition based on the input medical data, which further enriches every time consumers interact with the technology (Ram, 2018). Therefore, VNA have a realistic potential to outperform human doctors in setting official diagnosis and even offer sophisticated treatment recommendations where the self-care is applicable. The appearance of such advanced VNA in the marketplace depends on healthcare regulations rather than practical capabilities for its development in the next couple of years (Olson, 2018).
Inventors of the innovation believe that advanced VNA will change consumption patterns within the medical market (CognitionX, 2018). However, the success of the innovation is depended on the consumers’ decision, whether to adopt or reject it. The decision itself is influenced by many different variables, one of them exists within the consumer itself – personality traits. Thus, the purpose of this chapter is to define the extent at which personality traits, namely Innate Innovativeness (II) and Time Orientation (TO) influence consumers’ cognitive processing in regards of intentions to adopt the innovation in question.
Figure 1. Example of the Narrative of the VNA
Key Terms in this Chapter
Time Orientation (TO): Individual’s psychological predisposition towards mentally being to his/her perceived past, present or future time frames.
New Product Adoption (NPA): Acceptance and continues usage of a new product by a consumer.
Virtual Nurse Assistant (VNA): m-health technology powered by AI intended to provide healthcare services that are applicable without necessary intervention of human practitioners.
Artificial Intelligence (AI): Artificially created system that enables machines to learn and act just like humans.
Mobile Health (M-Health): A concept of healthcare practices provided through tablets and cell phones.
Relative Advantage (RA): In this chapter perceived relative advantage is defined as consumer’s subjective advantageous perception attributable to a product usage.
Perceived Risk (PR): Consumer’s subjective risk perception attributable to a product usage.
Behavioural Intention (BI): Decision to act in a particular way, that is yet to be actualised.
Innate Innovativeness (II): Individual's generalised unobservable trait reflecting inherently innovative personality, predisposition and cognitive style.
Domain-Specific Innovativeness (DSI): The tendency of the consumer to acquire information and new products within a specific product domain.
Product Attributes (PA): Product’s tangible (e.g., size, colour) and intangible (e.g., reliability, brand equity) characteristics.